• Structural Engineering and Mechanics
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• v.64 no.6
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• pp.803-817
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• 2017

Structural health monitoring has increasingly been a focus within the civil engineering research community over the last few decades. With increasing application of sensor networks in large structures and infrastructure systems, effective use and development of robust algorithms to analyze large volumes of data and to extract the desired features has become a challenging problem. In this paper, we grasp some precautions and key points of the signal processing approach, wavelet, establish a relative reliable framework, and analyze three problems that require attention when applying wavelet based damage detection approach. The cases studies how to use optimal scales for extracting mode shapes and modal curvatures in a reinforced concrete beam and how to effectively identify damages using maximum curves of wavelet coefficient differences. Moreover, how to make a recognition based on the wavelet multi-resolution analysis, wavelet packet energy, and fuzzy sets is a meaningful topic that has been addressed in this work. The relative systematic work that compasses algorithms, structures and evaluation paves a way to a framework regarding effective structural health monitoring, orientation, decision and action.

• Smart Structures and Systems
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• v.21 no.4
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• pp.421-433
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• 2018

Carbon fiber reinforced polymer (CFRP) cable has good mechanical properties and corrosion resistance. However, the anchorage of CFRP cable is a big issue due to the anisotropic property of CFRP material. In this article, a high-efficient bonding anchorage with novel configuration is developed for CFRP cables. The acoustic emission (AE) technique is employed to evaluate the performance of anchorage in the fatigue test and post-fatigue ultimate bearing capacity test. The obtained AE signals are analyzed by using a combination of unsupervised K-means clustering and supervised K-nearest neighbor classification (K-NN) for quantifying the performance of the anchorage and damage evolutions. An AE feature vector (including both frequency and energy characteristics of AE signal) for clustering analysis is proposed and the under-sampling approaches are employed to regress the influence of the imbalanced classes distribution in AE dataset for improving clustering quality. The results indicate that four classes exist in AE dataset, which correspond to the shear deformation of potting compound, matrix cracking, fiber-matrix debonding and fiber fracture in CFRP bars. The AE intensity released by the deformation of potting compound is very slight during the whole loading process and no obvious premature damage observed in CFRP bars aroused by anchorage effect at relative low stress level, indicating the anchorage configuration in this study is reliable.

• Smart Structures and Systems
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• v.15 no.3
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• pp.699-715
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• 2015

A modal parameter based damage sensitive feature (DSF) is defined to mimic the relative change in any diagonal element of the stiffness matrix of a model of a structure. The damage assessment is performed in a statistical pattern recognition framework using empirical complementary cumulative distribution functions (ECCDFs) of the DSFs extracted from measured operational vibration response data. Methods are discussed to perform probabilistic structural health assessment with respect to the following questions: (a) "Is there a change in the current state of the structure compared to the baseline state?", (b) "Does the change indicate a localized stiffness reduction or increase?", with the latter representing a situation of retrofitting operations, and (c) "What is the severity of the change in a probabilistic sense?". To identify a range of normal structural variations due to environmental and operational conditions, lower and upper bound ECCDFs are used to define the baseline structural state. Such an approach attempts to decouple "non-damage" related variations from damage induced changes, and account for the unknown environmental/operational conditions of the current state. The damage assessment procedure is discussed using numerical simulations of ambient vibration testing of a bridge deck system, as well as shake table experimental data from a 4-story steel frame.

• Smart Structures and Systems
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• v.14 no.2
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• pp.159-189
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• 2014

One of the most important requirements in the evaluation of existing structural systems and ensuring a safe performance during their service life is damage assessment. Damage can be defined as a weakening of the structure that adversely affects its current or future performance which may cause undesirable displacements, stresses or vibrations to the structure. The mass and stiffness of a structure will change due to the damage, which in turn changes the measured dynamic response of the system. Damage detection can increase safety, reduce maintenance costs and increase serviceability of the structures. Artificial Neural Networks (ANNs) are simplified models of the human brain and evolved as one of the most useful mathematical concepts used in almost all branches of science and engineering. ANNs have been applied increasingly due to its powerful computational and excellent pattern recognition ability for detecting damage in structural engineering. This paper presents and reviews the technical literature for past two decades on structural damage detection using ANNs with modal parameters such as natural frequencies and mode shapes as inputs.

• Smart Structures and Systems
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• v.18 no.3
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• pp.525-540
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• 2016

The CFRP-confined circular concrete-filled steel tubular column is composed of concrete, steel, and CFRP. Its failure mechanics are complex. The most important difficulties are lack of an available method to establish a relationship between a specific damage mechanism and its acoustic emission (AE) characteristic parameter. In this study, AE technique was used to monitor the evolution of damage in CFRP-confined circular concrete-filled steel tubular columns. A fuzzy c-means method was developed to determine the relationship between the AE signal and failure mechanisms. Cluster analysis results indicate that the main AE sources include five types: matrix cracking, debonding, fiber fracture, steel buckling, and concrete crushing. This technology can not only totally separate five types of damage sources, but also make it easier to judge the damage evolution process. Furthermore, typical damage waveforms were analyzed through wavelet analysis based on the cluster results, and the damage modes were determined according to the frequency distribution of AE signals.

• Smart Structures and Systems
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• v.23 no.5
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• pp.507-520
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• 2019

In the structural health monitoring field, damage detection has been commonly carried out based on the structural model and the engineering features related to the model. However, the extracted features are often subjected to various errors, which makes the pattern recognition for damage detection still challenging. In this study, an automated damage identification method is presented for hanger cables in a tied-arch bridge using a convolutional neural network (CNN). Raw measurement data for Fourier amplitude spectra (FAS) of acceleration responses are used without a complex data pre-processing for modal identification. A CNN is a kind of deep neural network that typically consists of convolution, pooling, and fully-connected layers. A numerical simulation study was performed for multiple damage detection in the hangers using ambient wind vibration data on the bridge deck. The results show that the current CNN using FAS data performs better under various damage states than the CNN using time-history data and the traditional neural network using FAS. Robustness of the present CNN has been proven under various observational noise levels and wind speeds.

• Journal of the Korean Geotechnical Society
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• v.20 no.7
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• pp.15-24
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• 2004

This paper describes a GIS-based assessment of residential building damage caused by the 1994 Northridge earthquake in which the fractions of existing buildings damaged at various percentages of replacement cost are related to a range of seismic parameters. The assessment uses data from safety inspection reports and tax assessor records, both of which were geocoded and linked to seismic parameters derived from strong motion records at 164 different sites. The paper also describes a GIS-based pattern recognition algorithm for identifying locations of most intense building damage. The algorithm provides a framework for rapidly screening remote sensing data and dispatching emerging services.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.3
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• pp.747-754
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• 2014

In spite of its usefulness for health monitoring of structures on steady external load, the statistical pattern recognition technology (SPRT), based on Mahalanobis distance theory (MDT), is not good enough for the health monitoring of structures on large variability external load like earthquake. Damage is usually determined by the difference between the average measured value of undamaged structure and the measure value of damaged one. So when external variability gets larger, the difference gets bigger along, which is thus easily mistaken for a damage. This paper aims to overcome the problem and develop an improved Mahalanobis distance theory (IMDT), that is, a SPRT with revised MDT in order to decrease external variability so that we will be able to continue to monitor the structure on uncertain external variability. This method is experimentally tested to see if it precisely evaluates the health of a cable-stayed bridge on each general random load and earthquake load. As a result, the IMDT is found to be valid in locating structural damage made by damaged cables by means of data from undamaged cables. So it is proved to be effectively applicable to the health monitoring of bridges on external load of variability.

• Journal of Korean Medical classics
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• v.25 no.4
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• pp.23-38
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• 2012

Objective : Generally speaking Reverting yin disease pattern(厥陰病) is the last step in cold damage(傷寒). Therefore recognized Yin cold disease(陰寒病) is increasing, and resist action One Yang qi(一陽) began to creep into body. But Ke Qin(柯琴) have a different way of thinking that Reverting yin disease pattern connected with the loss of Liver's function. Liver qi depression(肝鬱) make a ministerial fire(相火), and it make a nutrient and blood insufficiency(營血不足). Method : I will try to describe the Sanghanlun's Reverting yin disease pattern through the Ke-qin's JueyinbingJie(厥陰病解), and I would like to point out that the exact meaning of Reverting yin(厥陰) is connected with Liver's ministerial fire. Result : Ke Qin's JueyinbingJie explained the Reverting yin disease pattern was connected with Liver(肝), and according to Six qi theory(六氣學說) connected with ministerial fire, and according to meridian and Collateral theory(經絡學說) connected with closing referring to inward actions(闔) among the Opening closing and pivot(關闔樞). Conclusion : Ke Qin was recognized that Reverting yin disease pattern have relevance to the loss of Liver's function. In other world, It is connect with soothe the liver and purge fire(疏肝瀉火) and nutrient and blood insufficiency(營血不足).

• Transactions of the Korean Society of Machine Tool Engineers
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• v.14 no.3
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• pp.87-95
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• 2005

The object of this study is to investigate the effect of joinability by using acoustic emission(AE) signals and doing a pattern recognition for weld heat affected zone(HAZ) in tensile testing. This study was carried out an SWS 490A high tension steel for electric shielded metal arc welding(SMAW), $CO_2$ gas arc welding and TIG welding. And correspondingly, the root openings are 3, 4 and 2.8mm. The results of the tensile test of weld HAZ come out electric shield arc welding $>\;CO_2$ gas arc welding > TIG welding in case of single welding. It is believed that this is a phenomenon where difference of its root opening or base metal thickness. Also, the technique of AE is ideally suited to study variables which control time and stress dependent fracture or damage process in metallic materials.